On Estimation of Autoregressive Signals in the Presence of Noise

Abstract

Estimation of autoregressive (AR) signals measured in white noise is considered. A well-known fact is that the measurement noise causes the least-squares (LS) estimate of the AR parameters to be biased. The kernel of an alternative method to be proposed is that, unlike the previous LS-based methods, a noniterative estimation scheme is established for the measurement white noise variance - the source of the bias. Numerical results demonstrate that the proposed method is much more cost effective in terms of computations and accuracy than the previous LS-based methods. The establishment of this noniterative unbiased estimation method also provides a mechanism for better understanding of the family of the LS-based methods